Advanced Threats in AI and Data Security

Overview

This unit provides students with essential knowledge and practical skills in securing data and AI systems. It covers core topics such as database security, privacy, and copyright, as well as emerging threats like adversarial attacks, inference attacks, and model poisoning. In addtion the unit covers responsible AI priniciapls and framework. Students will learn to identify vulnerabilities, implement protection mechanisms, and evaluate the effectiveness of security solutions in real-world scenarios. They also learn how to desing IT and Cybersecurity polices and controls.

Unit learning outcomes

Students who successfully complete this unit will be able to:

1. Analyse and evaluate the fundamental principles of data security, including confidentiality, integrity, availability, privacy, and intellectual property protection, and articulate their application in complex, real-world scenarios
2. Analyse scenarios and apply data-driven security tools to investigate and mitigate risks in realistic contexts, including protecting sensitive data, securing databases, and defending machine learning and AI models against security threats and associated ethical implications
3. Critically evaluate data and AI scenarios for technical robustness, security, and socio-technical implications, demonstrating ethical and legal judgement while applying advanced expertise in line with professional cybersecurity standards
4. Recommend and justify policies and controls to mitigate risks in diverse social and business contexts, incorporating socio-technical considerations, including ethics and perspectives from Indigenous cultures
5. Develop and critically apply advanced knowledge and expertise in the AI domain, including intelligence concepts, cyber threats, and responsible AI principles, to address complex, real-world challenges

Teaching methods

Hawthorn

Assessment

Content

• Database Vulnerabilities and Protection
• Confidentiality, Integrity, and Availability
• Privacy and Differential Privacy Techniques
• Societal and Legal Issues
• Copyright and Data Ownership
• Malware Analysis and Network Forensics
• Adversarial Attacks and Mitigation Techniques
• Inference Attacks and Defence Mechanisms
• Data Poisoning Attacks and Robustness
• Generative AI Threats
• Responsible AI
• IT and AI Policies and controls